Decision support system for data-driven maintenance of rolling stock
A recent McKinsey survey on most European railway operators states that fifty per cent of their knowledge-workforce will retire within the next ten years. To stay operational, executives must think on how to operate their future organization and integrate a pro-active knowledge-management within its processes.
In practice, machine learning and artificial intelligence can manage big data from sensors that deliver a health-state-image from the rolling stock (i.e. locomotive, wagon) and learn from the experienced maintenance decisions of the knowledge-workers day by day, thus making “core”-knowledge available and reproducible. With every experienced decision, artificial intelligence and machine learning improve their knowledge base and can start to make suggestions and assist in failure detection and problem solving.
LeanBI and Prose, the Swiss specialist for Mobility Engineering, jointly develop a decision support system for data-driven maintenance of rolling stock. A first implementation will be operationalized for the Swiss operator “zb Zentralbahn AG”. Gerhard Züger, COO of “Zentralbahn”, emphasizes the financial impact of this strategic digitalization initiative: “Digitalization will help us reduce maintenance costs and thus increase our competitiveness in the near future.”
The Canton Berne Economic Development Agency supports this digital initiative with its financing Covid-19 program based on industrial location promotion guidelines for research & development projects.
In the past, maintenance organizations that have been optimized on planned activities and have triggered their asset (locomotives and/or wagons) to get maintained on a timely or distance basis understood the world from a descriptive point of view. Within a MIT (Massachusetts Institute of Technology) program “Digital Business Strategy: Harnessing our digital future”, PROSE designed a digitalized asset management world, where the asset (i.e. the locomotive) itself informs the maintenance organization.
In a prescriptive paradigm, maintenance activities base on a predicted system status, and give an availability forecast through the analysis of pattern and trends. Only agile organizations and processes can manage this paradigm change, supported by digitalized maintenance system that generates solution proposals and decision support.
The financial side of the business model increases by maximizing availability of the asset due to a digitalized and ongoing health-state-image of the asset that not only triggers maintenance base on the actual condition of the asset but predicts the maintenance need in the future. If the availability of the individual asset is higher, less asset in total is necessary to fulfill a transportation need. Thus, asset investments are reduced for operators.
For LeanBI and PROSE we see digitalization as a strategic move for the Swiss operator market. Due to a significant income drop in times of Covid-19 in the market segment operator, cost efficient maintenance becomes the key topic.
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